Mining frequent closed rooted trees

Many knowledge representation mechanisms are based on tree-like structures, thus symbolizing the fact that certain pieces of information are related in one sense or another. There exists a well-studied process of closure-based data mining in the itemset framework: we consider the extension of this process into trees. We focus mostly on the case where labels on the nodes are nonexistent or unreliable, and discuss algorithms for closurebased mining that only rely on the root of the tree and the link structure. We provide a notion of intersection that leads to a deeper understanding of the notion of support-based closure, in terms of an actual closure operator. We describe combinatorial characterizations and some properties of ordered trees, discuss their applicability to unordered trees, and rely on them to design efficient algorithms for mining frequent closed subtrees both in the ordered and the unordered settings. Empirical validations and comparisons with alternative algorithms are provided.Postprint (author’s final draft

EvoMiner: Frequent Subtree Mining in Phylogenetic Databases

The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees --- two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set

Graph-based task libraries for robots: generalization and autocompletion

In this paper, we consider an autonomous robot that persists over time performing tasks and the problem of providing one additional task to the robot's task library. We present an approach to generalize tasks, represented as parameterized graphs with sequences, conditionals, and looping constructs of sensing and actuation primitives. Our approach performs graph-structure task generalization, while maintaining task ex- ecutability and parameter value distributions. We present an algorithm that, given the initial steps of a new task, proposes an autocompletion based on a recognized past similar task. Our generalization and auto- completion contributions are eective on dierent real robots. We show concrete examples of the robot primitives and task graphs, as well as results, with Baxter. In experiments with multiple tasks, we show a sig- nicant reduction in the number of new task steps to be provided

Mining substructures in protein data

In this paper we consider the 'Prions' database that describes protein instances stored for Human Prion Proteins. The Prions database can be viewed as a database of rooted ordered labeled subtrees. Mining frequent substructures from tree databases is an important task and it has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. This has given rise to the development of many tree mining algorithms which can aid in structural comparisons, association rule discovery and in general mining of tree structured knowledge representations. Previously we have developed the MB3 tree mining algorithm, which given a minimum support threshold, efficiently discovers all frequent embedded subtrees from a database of rooted ordered labeled subtrees. In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type. Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge. This aspect will be demonstrated by providing an analysis of the extracted frequent subtrees with respect to discovering interesting protein information. Furthermore, the minimum support threshold can be used as the controlling factor for answering specific queries posed on the Prions dataset. This approach is shown to be a viable technique for mining protein data